AIGCodeSet: Distinguishing AI and Human Code

As technology evolves, so does the way we write code. Large language models (LLMs), a type of artificial intelligence, have made it easier for both professionals and students to generate code quickly. These AI systems can help developers wrap up their tasks much faster, which sounds great, but they also raise some eyebrows in educational settings. Many educators are now wondering, "Who really wrote that code?" To tackle this issue, a new dataset called AIGCodeSet has been introduced to help identify whether code is generated by AI or a human.

#The Rise of AI in Coding

Generative AI has taken the coding world by storm. According to some studies, developers can complete tasks up to double as fast when using AI tools. Imagine finishing your homework in half the time – sounds too good to be true, right? In fact, surveys show that some developers feel a 33% boost in their productivity. For students, AI tools provide an array of helpful resources, such as sample solutions, ways to solve problems, code reviews, and more.

However, not everything is sunshine and rainbows. There are concerns about academic dishonesty, plagiarism, and even security vulnerabilities in the code produced by AI. Reports have indicated that AI-generated Code could be full of bugs or security issues, which is as comforting as a leaky umbrella in a rainstorm. Relying solely on AI can even cause developers to lose their coding skills or, worse, their jobs.

#Why Do We Need AIGCodeSet?

With these concerns in mind, researchers have started looking into how to differentiate between AI-generated and Human-written Code. Most of the previous work focused solely on coding from scratch, while AIGCodeSet aims to cover various scenarios, such as fixing errors in existing code.

This new dataset focuses specifically on Python, one of the most popular programming languages. AIGCodeSet consists of a mix of human-written and AI-generated code, allowing researchers to examine the differences between the two. This dataset has gathered over 7,500 code samples taken from various problems, providing a solid foundation for further studies.

#How AIGCodeSet Was Created

Creating AIGCodeSet began by collecting programming problems and human-written code from a large dataset called CodeNet, which contains millions of examples across different programming languages. Researchers selected 317 problems to focus on, ensuring a variety of challenges to tackle.

From each problem, they grabbed five examples of human-written code – some of which were correct, while others had errors or produced incorrect results. In total, they extracted about 4,755 human-written code samples. This diverse selection allows researchers to compare the quality and style of the code generated by AI with that written by real humans.

#Generating AI Code

To generate AI code, researchers used three specific LLMs: CodeLlama, Codestral, and Gemini. Each model was tasked with producing code in three different ways for each problem:

1. Writing code from scratch based on the problem description.
2. Correcting human-written code that had runtime errors.
3. Fixing code that produced the wrong output.

After generating the AI code, researchers sifted through the results to filter out any outputs that weren’t relevant, ensuring high-quality samples for the dataset. In the end, around 2,828 AI-generated code snippets were included.

#What Makes AIGCodeSet Different?

AIGCodeSet stands out because it covers a range of scenarios that other datasets haven’t addressed. While many previous studies focused solely on having the AI generate code from scratch, this dataset includes cases where AI is used to fix errors. That’s a step up in understanding how AI can be utilized in real-world coding situations.

Moreover, AIGCodeSet provides a rich resource for researchers to study AI-generated code Detection Methods. With its combination of human and AI-generated samples, researchers can evaluate how effectively different methods can distinguish between the two.

#Testing the Dataset

To see how well the dataset performs, researchers applied various code detection methods. They trained different models and assessed their performance in identifying whether code was written by a human or generated by AI.

The results revealed that the performance varied depending on which LLM was used to generate the code. Some models performed better than others, but overall, certain methods for detecting AI-generated code outshined others.

#What Did Researchers Learn?

From their experiments, researchers made a few interesting observations:

1. Different Styles: Human-written code was generally longer than AI-generated code. Some AI models were more likely to use functions, while others incorporated more blank lines and comments, mimicking human styles.

2. Scenarios Matter: Detection accuracy heavily depended on whether the AI code was generated from scratch or if it involved fixing human code. When fixing errors, AI tends to mimic human coding styles closely, making it trickier to identify.

3. Model Performance: The Bayes classifier was particularly effective in distinguishing between AI-generated and human-written code. Meanwhile, one of the AI models, Gemini, produced code that resembled human code closely, making it tougher to detect.

#Conclusion

AIGCodeSet is a much-needed resource in the ever-evolving landscape of coding and AI. By providing a comprehensive dataset that includes various scenarios of AI-generated code, researchers are now better equipped to address concerns about authorship and academic integrity. As the use of AI becomes more prominent, understanding how to identify AI-generated content will be crucial.

In the future, researchers plan to expand AIGCodeSet by including more programming languages and additional AI models. They also aim to investigate how real-world users, like students and developers, use these AI tools to generate code. By continually refining the dataset, the research community hopes to stay ahead of the curve in this rapidly changing field.

So, the next time you see a piece of code online, you might just wonder: Is it a clever human, or a genius AI at work? With resources like AIGCodeSet, we can start to find the answer. And who knows, maybe someday coding will just be a matter of saying, “Hey, AI, fix this for me!”